'More Events, More Medals, More Interest!': The Growth and Expansion of the Winter Olympic Games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The most recent Winter Olympics in Sochi witnessed an unprecedented addition of 12 new winter sports events. The result was a record 2800 athletes competing in 15 sports, comprising 98 events, and the awarding of 294 medals. At the first Winter Games in 1924, there were only 16 events and 49 medals awarded. By the time the 1988 Calgary Games rolled around, it had grown to 138 medals. This study traced the historical development in the growth of the Winter Games with comparisons made with the Summer Olympics. Primary and secondary written sources were examined and participant observation techniques were employed with the investigator attending a number Olympic Games. A review of the concept of “demonstration sports” was undertaken and how it has been replaced by the IOC evaluating new events with key factors being sport federation lobbying, television “friendliness”, the public appeal of events, political issues (e.g. voting by IOC members on the dropping and adding of new sports) and social factors (e.g. gender balance). It was found that the growth of the Winter Olympics program can be attributed to several key reasons: the new Olympic cycle when the IOC made its decision to put the Winter and Summer Games on off-setting schedules; the obvious television appeal of many of the new events; gender balance with more women’s events and now more mixed events getting the competitors closer to a state of equilibrium; and pressure from the X Games as the IOC tries to keep pace with the times and maintain a connection to the younger generation. With the Winter Games moving on to Pyeongchang for its 2018 edition, the study concludes with an analysis of what new developments are possibly in store.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it